The present invention relates to a radar signal digital processor which can suppress clutter disturbances and enhance useful signals of given Doppler frequency. It can approximate the principle of operation of the optimum processor, which is the processor which maximizes the signal-to-noise ratio. It is furthermore capable of suppressing, in an adaptive way, clutter having an approximatively Gaussian power spectrum. Its performance is better than that of an adaptive moving target indicator MTI (MTIA) followed by a coherent integrator.
The invention presented herein relates to the field of radars, and more exactly that of radar signal processing for disturbance suppression. The device may be inserted within an integrated radar signal digital processor, between the analogue-to-digital converter and the modulus extractor. The specific tasks of the device (disturbance suppression and useful signal enhancement), are obtained in an adaptive manner as far as disturbance and useful signal to be detected (parameter variations) are concerned.
This adaptiveness provides a way to overcome the limitations related to fixed disturbance filtering, while optimizing, at the same time, system capability to detect useful targets. Signal processors, which are presently implemented by means of digital circuitry, receive at their input the radar video signal which is detected coherently and coverted into digital format. They provide as an output an indication of targets which may be present (detection process) by attenuating undesirable signals. The elimination of undesirable signals arising out of reflections caused by natural or artificial obstacles (clutter), is obtained in current radar technique by means of MTI filtering (Moving Target Indicator) described in many articles and books, among them:
[1] M. I. Skolnik: "Introduction to Radar Systems" 2.sup.nd Edition, Mc Graw Hill, New York; 1980; PA0 [2] D. C. Schleher "MTI Radar", Artech House 1978. PA0 [3] C. E. Muehe: "Moving Target Detector, An Improved Signal Processor", AGARD Conf. Proceedings No. 195, June 14-17, 1976, pp. 14-1 to 14-10; PA0 [4] R. M. O'Donnel--C. E. Muehe: "Automated Tracking For Aircraft Surveillance Radar Systems"--IEE Trans., Vol. AES 15, No. July 1979, pp. 508-516; PA0 [5] D. Karp--J. R. Anderson: "MTD-II Summary Report" MIT--Lincoln Lab. ATC 95, June 1, 1981; PA0 [6] J. R. Anderson--D. Karp: "Evaluation of the MTD in High Clutter Environment". IEEE Int. Radar Conf., 1980, pp. 219-224; PA0 [7] J. W. Taylor: "Sacrifices in Radar Clutter Suppression. PA0 [8] E. D'Addio, G. Galati, E. Giaccari Selenia S.p.A.: "Digital A-MTD Processor for Search Radars having a set of Doppler filters and a threshold system selectable upon type of disturbance, Italian patent No. 48701/A/83, where the task is that of selecting one of three filter shapes, each of which is designed for a given level of the disturbance to be suppressed, as a function of the measured clutter level. PA0 [9] G. Galati, P. Lombardi "Design & Evaluation of an Adaptive MTI filter," IEEE Trans. on AES Vol. 14 No. 6 Nov. 1978. PA0 [10] L. E. Brennan, I. S. Reed: "Optimum processing of unequally spaced radar pulse trains for clutter rejection". IEEE Trans. on Aerospace and Electronic System, vol. AES--4, No. 3, May 1968 pp. 474-477. PA0 [11] F. Chiuppesi, G. Galati, P. Lombardi: "Optimisation of rejection filters", IEEE Proc., Vol. 127, Pt. F, No. 5, October 1980, pp. 354-359. PA0 [12] V. G. Hansen: "Clutter suppression in search radar" IEEE Conf on Decision and Control, 1977, pp. 536-543. PA0 [13] V. G. Hansen, D. Michelson: "A comparison of performance against clutter of optimum, pulsed doppler and MTI processors". IEEE Int Radar Conference 1980, pp. 211-218. PA0 [14] Papoulis "Probability, Random Variables and Stochastic Processes" Boringhieri 1973, the optimum weights provided by the theory, in the case of known Doppler frequency f.sub.D of the useful signal, are: EQU W=M.sup.-1 S* (5) PA0 where the asterisk stands for the complex conjugate of the number to which it applies while S indicates the set of samples of the expected useful signal. In formulae we have: EQU S=S.sub.1, S.sub.2 . . . S.sub.NC ( 6) PA0 Through (5) it is possible to adapt weights W and therefore filter response, to the disturbance parameters. The greater part of the known clutter disturbances may be modelled by a power density spectrum. Therefore the generic element (i,k) of matrix M has the following expression: EQU M.sub.i,k =P.sub.c .rho..sup.(i-k).spsp.2 exp {j(i-k).phi..sub.c }+P.sub.N .delta..sub.ik ( 8) PA0 When P.sub.C &gt;&gt;P.sub.N, which always holds true in practical cases, from (8) and (9) it follows that matrix M depends exclusively on .rho. and .phi..sub.c, less for a non relevant multiplying constant. PA0 Calculation of the inverse matrix M.sup.-1 as required by (5) is an unfeasible operation, in real time, with present technologies. PA0 The invention herein overcomes this problem in the following manner: PA0 Under the assumption that .phi..sub.c =0, the corresponding inverse matrix is a function of the autocorrelation coefficient. If .phi..sub.c .noteq.0 it is possible to derive M.sup.-1 (.rho.,.phi..sub.c) knowing M.sup.-1 (.rho.,0): EQU M.sup.-1.sub.i,k (.rho.,.phi..sub.c)=M.sup.-1.sub.i,k (.rho.,0).multidot.exp {J(K-i).phi..sub.c } (10) PA0 Assuming that the autocorrelation coefficient .rho. may take on N distinct values, it is possible to store, once for ever, the elements of M.sup.-1 (.rho.,0) for each of the N expected values.
In current practice, it is customary to resort to a more flexible type of filtering than the one above, which is that of 1.sup.st and 2.sup.nd generation MTD, described in Chapter 4.7 of Skolnik's book and in many other works, among them:
The technological improvements achieved of recent years have given way to the development of devices having an improved adaptivity. An example of this is the A-MTD processor, such as that described in:
A further example in this area can be found in the adaptive MTI device:
This latter device implements the function of a normal MTI filter over clutter disturbance caused by moving obstacles by means of an estimate of the disturbance Doppler phase.
The limits, in performance terms, of the processors mentioned above are as follows:
1. The fixed type of filtering, because pre-designed to cater to a specific disturbance situation, degrades performance obtainable in an environment which has different disturbance characteristics.
2. The degree of freedom, typically the number of processed pulses, are not exploited effectively in the partitioning of pulses between disturbance suppression and useful signal integration.
3. The adaptive type of filtering, mentioned above, gives way to only a partial adaptation to the unknown characteristics of the disturbance. In one case we adapt to its power level, in another we adapt to its average Doppler frequency. In both cases there is no adaptation to the shape of the disturbance power spectrum.